Applying machine learning on patient-reported data to model the selection of appropriate treatments for low back pain: A Pilot Study
| Autor: | Oude Nijeweme - d'Hollosy, Wendy, van Velsen, Lex, Poel, Mannes, Groothuis-Oudshoorn, Catharina, Soer, Remko, Stegeman, Patrick, Hermens, Hermie, Cabitza, Federico, Fred, Ana, Gamboa, Hugo |
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| Přispěvatelé: | Biomedical Signals and Systems, Datamanagement & Biometrics, Health Technology & Services Research |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Classification algorithms
business.industry Computer science Systems Fleiss' kappa Clinical decision support Machine learning computer.software_genre Clinical decision support system Low back pain Cross-validation Test (assessment) Learning effect Inter-rater reliability Statistical classification medicine Artificial intelligence medicine.symptom business computer |
| Zdroj: | Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020), 5: HEALTHINF, 117-124 Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies: Vol. 5: HEALTHINF, 117-124 STARTPAGE=117;ENDPAGE=124;TITLE=Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies HEALTHINF |
| Popis: | The objective of this pilot study was to determine whether machine learning can be applied on patient-reported data to model decision-making on treatments for low back pain (LBP). We used a database of a university spine centre containing patient-reported data from 1546 patients with LBP. From this dataset, a training dataset with 354 features (input data) was labelled on treatments (output data) received by these patients. For this pilot study, we focused on two treatments: pain rehabilitation and surgery. Classification algorithms in WEKA were trained, and the resulting models were validated during 10-fold cross validation. Next to this, a test dataset was constructed - containing 50 cases judged on treatments by 4 master physician assistants (MPAs) - to test the models with data not used for training. We used prediction accuracy and average area under curve (AUC) as performance measures. The interrater agreement among the 4 MPAs was substantial (Fleiss Kappa 0.67). The AUC values i ndicated small to medium (machine) learning effects, meaning that machine learning on patient-reported data to model decision-making processes on treatments for LBP seems possible. However, model performances must be improved before these models can be used in real practice. |
| Databáze: | OpenAIRE |
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